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Companies spend millions on AI, but profits do not grow. Research shows that even the best models fail most work tasks. What happens between creating the technology and monetizing it?
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What happened
A new wave of AI euphoria has swept the world. Companies are investing billions of dollars in language models, promising a revolution in every business process. But recent research delivers a harsh diagnosis: the technology is not living up to expectations. An experiment involving AI agents from OpenAI, Anthropic, and Google DeepMind showed failure in 80% of tasks performed by bankers, consultants, and lawyers. Models code excellently, but fail at strategic decisions and work with real data. The main problem is that no one understands what to do on the second step between creating the technology and making money.
How this is useful for business
The crisis of expectations creates incredible opportunities for those who see the real picture. While competitors blindly implement AI and lose money on failed projects, smart entrepreneurs can occupy the niche of honest consultants and providers of working solutions. The market is hungry for practices that show concrete results, not presentations with growth charts. Companies will start looking for those who promise not loud slogans, but measurable ROI. This is the time for niche experts who understand the limitations of the technology and know how to embed it into existing processes.
How to make money from this
You can make money from AI chaos in three ways. The first is helping companies choose the right tasks for automation. The second is creating hybrid solutions where AI handles routine work and people handle strategy. The third is training teams to work effectively with language models. Each path requires a deep understanding of technology and business processes. Clients are ready to pay $5,000-50,000 for an AI strategy audit and $3,000-15,000 monthly for support. The main thing is not to sell empty promises, but to demonstrate concrete metrics before and after implementation.
Business ideas
1. AI auditor for small businesses. You review a client's processes, identify tasks where AI really saves time, and implement targeted solutions. Subscription $299-999/month.
2. Prompt marketplace for industries. You sell ready-made templates for lawyers, doctors, and real estate agents. One prompt: $19-49, library subscription: $49/month.
3. Hybrid analytics service. AI processes the data, a human checks and corrects the conclusions. Contracts from $8,000/month for a team of 3 analysts.
4. AI training for corporations. Corporate workshops for 20-40 people. Price: $15,000-30,000 per event, recurring sessions: $5,000/month.
5. AI project validation platform. You test clients' business hypotheses with AI before large-scale implementation. Fixed fee: $2,000-7,000 per project.
Risks and limitations
The main danger is that the market may change sharply in 12-18 months when models appear that solve current problems. Regulatory pressure is growing: the EU is tightening rules, and this creates uncertainty for international projects. Competition is intensifying every day, as IT giants launch their own business solutions. The technology requires constant knowledge updates: what works today may become outdated in a quarter. Success depends on the ability to adapt quickly and maintain cutting-edge expertise.
7-day action plan
Day 1-2: Study three cases of AI failures in business from open sources. Make a list of typical implementation mistakes. Day 3: Identify one industry where you have expertise or connections. Study its specific tasks. Day 4: Test 3-5 AI tools on real tasks from the selected industry. Record the results. Day 5: Create a simple service offer, such as an audit or consultation. Calculate the economics for the client. Day 6: Find 10 potential clients through LinkedIn or industry communities. Send personalized messages. Day 7: Conduct the first free consultation, get feedback, and adjust the offer.
Original news: MIT Technology Review · See other news in the news section.
